representing the likelihood of seeing observation \(x_t\)  at timestep \(t\)  given the two possible feature states. As explained by Juang and Rabiner [1]}, the stochastic process represented by \(X_T\)  can then be fully characterized by the HMM parameters \(a\) , \(A\)  and \(B(x_t)\) , without requiring the knowledge of the sequence of hidden states.
